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OmniCapBench: A Deep-Structured Evaluation Framework for Fine-Grained Audio-Visual Captioning

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Briefings

OmniCapBench introduces a deep-structured evaluation framework for fine-grained audio-visual captioning, replacing traditional free-form text scoring with atomic, verifiable units across entity references, visual shots, and audio events. The benchmark utilizes 786 densely annotated videos to distinguish MLLM perception errors such as temporal grounding failures, identity drift, and cross-modal misalignment. This approach provides a fine-grained roadmap for improving omnimodal development by exposing specific capability limits in frontier models.

Key points

• OmniCapBench introduces a deep-structured evaluation framework that replaces free-form text scoring with atomic, verifiable units across entity references, visual shots, and audio events.
• The benchmark uses 786 densely annotated videos to distinguish specific MLLM perception errors including temporal grounding failures, identity drift, and cross-modal misalignment.

Context

Multimodal large language models are rapidly evolving toward continuous audio-visual reasoning, creating an urgent need for evaluations that expose their capability limits. Audio-visual captioning is an ideal diagnostic task, yet current benchmarks face a coupled trade-off: whole-caption scores provide coverage without localization, local probes provide localization without coverage, and unconstrained LLM judges introduce instability.

Limitations

Coverage is limited to the 786 densely annotated videos used in the evaluation. The benchmark does not provide causal claims or specific causal mechanisms for observed errors, and results are based on snapshot data from a single study period.

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